Preference densities and social choices

We discuss a ranking method that allows social pairwise rankings of alternatives to depend on more than just individuals’ pairwise rankings. This violates Arrow’s Independence of Irrelevant Alternatives, but allows Borda’s rank-order counting, which provides a limited accounting for individual preference intensities. We capture Arrow’s rules (i.e., with IIA) and Borda’s method as two polar cases, and allow cases in between. Our main result provides the critical line dividing those degrees of intensity, or preference density, that yield positive results from those that yield negative results. We thank two referees for helpful comments.     

中国城镇家庭财产水平研究:基于行为的视角

本文首先提出了一个各种因素影响家庭财产水平的理论框架。然后通过对奥尔多投资研究中心2007年"城市投资者行为调查问卷"数据的处理和实证分析,估算了中国城镇居民的财产函数,重点考察了户主的主观行为特征在财产积累过程中的重要作用。本文发现:户主投资参与度与风险偏好度的提高有利于家庭财产水平的增加;与低收入或者经济相对落后地区的家庭相比,对高收入或者经济发达地区的家庭来说,投资参与度与风险偏好度对家庭财产水平的积极影响更显著一些。最后,针对政府如何促进居民财产水平的提升,本文给出了相关的政策建议。     

上证180指数流动性效应的实证研究

本文从实证的角度探讨了上证180指数创立事件对其成份股流动性的影响以及进一步改善的方法。上证180指数的成立事件一方面促进了成份股价差水平和交易信息对称程度的改善,另一方面又促使交易深度水平和交易活跃性的降低。这些流动性效应是由于机构投资者对180成份股偏好所造成的:机构投资者增加了180成份股的需求同时减少了供给,就导致了交易深度和活跃性的降低;机构投资者作为相对知情交易者,它们的偏好增加了市场的需求竞争程度,从而降低了交易者之间的信息不对称程度以及改善了市场价差水平。根据国外的经验,ETF可以有效地增强市场交易深度,因此本文建议创立上证180指数ETF弥补180指数对深度方面的负面影响。     

作为内生偏好的利他行为及其经济学意义

现代经济学与现代生物学对人性的看法相当一致,都把自利看作人类行为的基本前提。但来自桑塔费学派的最新研究证明,在人类进化的早期阶段,利他行为作为社会规范内部化的产物,在维持个人之间的合作劳动、有效提高族群生存竞争能力方面,具有不可替代的重要作用。我们在ESS(即“进化稳定策略”)基础上提出一个演化均衡模型,进一步解释了利他行为的进化优势以及合作剩余导致利他偏好内生的机制。     

出生排序与行为偏好的经济实验研究

作为社会科学研究中的一个家庭背景指标,出生顺序和子女性别结构会影响人们的行为选择以及行为结果。文章以大学生为被试,采用实验室实验方法研究了出生顺序和子女性别结构对信任行为、风险偏好与竞争偏好的影响。实验研究发现：二胎的信任水平和竞争偏好都显著高于一胎。虽然二胎的风险偏好高于一胎,但统计上并不显著;同时,子女性别结构会影响出生顺序效应。在信任水平方面,混合结构下的二胎显著高于一胎,而同性结构下的出生顺序效应并不显著;在风险偏好与竞争偏好方面,同性结构下的二胎显著高于一胎,而混合结构下的出生顺序效应不显著。文章结论能够加深人们对不同出生顺序下个体行为偏好异质性的理解,具有一定的政策含义。     

我国基金重仓股选股偏好的时期似无关回归分析

基于基金重仓股季度面板数据,本文采用时期似无关回归模型分析了四大类共28个指标对基金持股比例的影响,并利用基金重仓股的统计值特点发现基金筛选股票的标准。研究结果表明,基金确实在寻求价值型投资,扩大基金规模可减轻股市投机行为;基金在调研阶段和操盘阶段对风险有不同的偏好,调研阶段规避风险,操盘阶段偏好高风险高回报;基金偏爱长期流动性好的股票;开放式基金的选股要求高于封闭式基金,牛市时基金的选股要求高于熊市;基金偏爱关注率高、信息丰富的股票;基金对行业的偏好基于行业的业绩表现;基金重仓股持股比例基本上与指数有相似的变化趋势。     

Machines learn neuromarketing: Improving preference prediction from self-reports using multiple EEG measures and machine learning

A basic aim of marketing research is to predict consumers’ preferences and the success of marketing campaigns at the population-level. However, traditional marketing tools have various limitations, calling for novel measures to improve predictive power. In this study, we use multiple types of measures extracted from electroencephalography (EEG) recordings and machine learning (ML) algorithms to improve preference prediction based on self-reports alone. Subjects watched video commercials of six food products as we recorded their EEG activity, after which they responded to a questionnaire that served as a self-report benchmark measure. Thereafter, subjects made binary choices over the food products. We attempted to predict within-sample and population level preferences, based on subjects’ questionnaire responses and EEG measures extracted during the commercial viewings. We reached 68.5% accuracy in predicting between subjects’ most and least preferred products, improving accuracy by 4.07 percentage points compared to prediction based on self-reports alone. Additionally, EEG measures improved within-sample prediction of all six products by 20%, resulting in only a 1.91 root mean squared error (RMSE) compared to 2.39 RMSE with questionnaire-based prediction alone. Moreover, at the population level, assessed using YouTube metrics and an online questionnaire, EEG measures increased prediction by 12.7% and 12.6% respectively, compared to only a questionnaire-based prediction. We found that the most predictive EEG measures were frontal powers in the alpha band, hemispheric asymmetry in the beta band, and inter-subject correlation in delta and alpha bands. In summary, our novel approach, employing multiple types of EEG measures and ML models, offers marketing practitioners and researchers a valuable tool for predicting individual preferences and commercials’ success in the real world.     

Modeling attitudes toward uncertainty through the use of the Sugeno integral

The aim of this paper is to present under uncertainty, and in an ordinal framework, an axiomatic treatment of the Sugeno integral in terms of preferences which parallels some earlier derivations devoted to the Choquet integral. Some emphasis is given to the characterization of uncertainty aversion.     

Hyperbolic discounting and the standard model: Eliciting discount functions

Experiments on static intertemporal choice find evidence of particularly extreme impatience toward immediate rewards. While this is often taken as support for hyperbolic discounting, it could also arise because the most likely participants in experiments may be those with the most immediate need for money. We conduct a calibration exercise and find that the extreme impatience observed in experiments can be accommodated by a standard exponential discounting model with no discounting and expectation of a ‘small’ increase in the base consumption level. The calibration uses existing estimates of curvature of utility.